During the bubble, a lack of profits was paradoxically an advantage for tech stocks. It removed traditional valuation metrics like P/E ratios that would have anchored prices to reality. This "valuation vacuum" allowed investors' imaginations and narratives to drive stock prices to speculative heights.
Related Insights
Michael Burry's thesis is that aggressive stock-based compensation (SBC) at companies like Nvidia significantly distorts their valuations. By treating SBC as a true owner's cost, a stock appearing to trade at 30 times earnings might actually be closer to 60 times, mirroring dot-com era accounting concerns.
Current AI-driven equity valuations are not a repeat of the 1990s dot-com bubble because of fundamentally stronger companies. Today's major index components have net margins around 14%, compared to just 8% during the 90s bubble. This superior profitability and cash flow, along with a favorable policy backdrop, supports higher multiples.
Today's massive AI company valuations are based on market sentiment ("vibes") and debt-fueled speculation, not fundamentals, just like the 1999 internet bubble. The market will likely crash when confidence breaks, long before AI's full potential is realized, wiping out many companies but creating immense wealth for those holding the survivors.
The memo argues that the "hysteria of the bubble" compresses the timeline for building out new technologies from decades into just a few years. Patient, value-focused investing would never fund the massive, parallel, and often wasteful experimentation required to jump-start a new technological paradigm at such a rapid pace.
The dot-com era was not fueled by pure naivete. Many investors and professionals were fully aware that valuations were disconnected from reality. The prevailing strategy was to participate in the mania with the belief that they could sell to a "greater fool" before the inevitable bubble popped.
The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.
This AI cycle is distinct from the dot-com bubble because its leaders generate massive free cash flow, buy back stock, and pay dividends. This financial strength contrasts sharply with the pre-revenue, unprofitable companies that fueled the 1999 market, suggesting a more stable, if exuberant, foundation.
At the bubble's peak, the market valued intangible, narrative-driven companies like eToys more than profitable, asset-heavy businesses like Toys R Us. Physical stores and cash-generating operations were seen not as assets but as an "albatross" weighing down stock prices in the new economy.
A macro strategist recalls dot-com era pitches justifying valuations with absurd scenarios like pets needing cell phones or a company's tech being understood by only three people. This level of extreme mania highlights a key difference from today's market, suggesting current hype levels are not unprecedented.
Marks argues that speculative bubbles form around 'something new' where imagination is untethered from reality. The AI boom, like the dot-com era, is based on a novel, transformative technology. This differs from past manias centered on established companies (Nifty 50) or financial engineering (subprime mortgages), making it prone to similar flights of fancy.
